A challenging aspect of cognitive systems and specifically
cognitive robots is the high parallelization of tasks with a vast
amount of incoming stimuli which need to be filtered to trigger
a suitable (re)action. We are able to focus our attention onto a
specific object or sound but we can also be easily distracted as
is observable in the ventriloquist effect, where humans' sound
perception is influenced by a doll being an additional visual
stimulus. Researchers thus developed algorithms to capture
the most salient cues in the scene but either on datasets [1]}
or under very constrained lab conditions [2]}. Although imple-
mentations using deep networks showed promising results in
those limited cases, DNN will have problems with changing
conditions in real-world environments due to retraining issues
inherent in closed set classification system.
A recent robotic experiment on indoor exploration proposed
a framework based on DNN to exploit the robot capabilities to
learn the unknown terrain in a human-like fashion using the
turtlebot platform [3]}. For the robot vision, a Kinect device
was mounted delivering depth information. Although one may
argue that Kinect sensors are error-prone and question their
reliability in a robust image capturing, the availability of the
authors' code enable other researchers to explore further the
advantages and pitfalls of the approach, identifying further
problems and possible solutions enriching discussions in the
cognitive robotic community for this important task.
Another important factor is that learning in humans is
not subject to one specific task for a specific point in time
and space but is continuous throughout life. Although DNNs
produce stable feature representations which may code for
more abstract concepts similar to neural codes along the brain
hierarchy, yet they are less flexible and do not allow online
adaptation nor any memory mechanisms. A new trend, how-
ever, to avoid retraining is using transfer learning: weights of
a deep network successfully trained on a set of e.g. images are
transfered to solve a similar task. A fruitful combination for
robot applications could be to join those strategies with open
set classification algorithms like the growing-when-required
networks (GWR, [4]}), which allow insertion and deletion of labels.
The benefit of a GWR is that it provides a computational
method to the question how actually learning is guided in the
absence of any labels. The robust representations emerging
from a deep neural network can thus complement the flex-
ible classification in environments beyond the restricted lab
conditions.
